Complement C3 variant and the risk of age-related macular degeneration

Background: Age-related macular degeneration is the most common cause of blindness in Western populations. Susceptibility is influenced by age and by genetic and environmental factors. Complement activation is implicated in the pathogenesis.Methods: We tested for an association between age-related macular degeneration and 13 single-nucleotide polymorphisms (SNPs) spanning the complement genes C3 and C5 in case subjects and control subjects from the southeastern region of England. All subjects were examined by an ophthalmologist and had independent grading of fundus photographs to confirm their disease status. To test for replication of the most significant findings, we genotyped a set of Scottish cases and controls.Results: The common functional polymorphism rs2230199 (Arg80Gly) in the C3 gene, corresponding to the electrophoretic variants C3S (slow) and C3F (fast), was strongly associated with age-related macular degeneration in both the English group (603 cases and 350 controls, P=5.9 x 10(sup -5)) and the Scottish group (244 cases and 351 controls, P=5.0 x 10(sup -5)). The odds ratio for age-related macular degeneration in C3 S/F heterozygotes as compared with S/S homozygotes was 1.7 (95% confidence interval [CI], 1.3 to 2.1); for F/F homozygotes, the odds ratio was 2.6 (95% CI, 1.6 to 4.1). The estimated population attributable risk for C3F was 22%.Conclusions: Complement C3 is important in the pathogenesis of age-related macular degeneration. This finding further underscores the influence of the complement pathway in the pathogenesis of this disease

Clinical Pharmacokinetics and Dose Recommendations for Posaconazole in Infants and Children.

OBJECTIVES: The objectives of this study were to investigate the population pharmacokinetics of posaconazole in immunocompromised children, evaluate the influence of patient characteristics on posaconazole exposure and perform simulations to recommend optimal starting doses. METHODS: Posaconazole plasma concentrations from paediatric patients undergoing therapeutic drug monitoring were extracted from a tertiary paediatric hospital database. These were merged with covariates collected from electronic sources and case-note reviews. An allometrically scaled population-pharmacokinetic model was developed to investigate the effect of tablet and suspension relative bioavailability, nonlinear bioavailability of suspension, followed by a step-wise covariate model building exercise to identify other important sources of variability. RESULTS: A total of 338 posaconazole plasma concentrations samples were taken from 117 children aged 5 months to 18 years. A one-compartment model was used, with tablet apparent clearance standardised to a 70-kg individual of 15 L/h. Suspension was found to have decreasing bioavailability with increasing dose; the estimated suspension dose to yield half the tablet bioavailability was 99 mg/m2. Diarrhoea and proton pump inhibitors were also associated with reduced suspension bioavailability. CONCLUSIONS: In the largest population-pharmacokinetic study to date in children, we have found similar covariate effects to those seen in adults, but low bioavailability of suspension in patients with diarrhoea or those taking concurrent proton pump inhibitors, which may in particular limit the use of posaconazole in these patients

A database of chlorophyll a in Australian waters

© The Author(s) 2018. Chlorophyll a is the most commonly used indicator of phytoplankton biomass in the marine environment. It is relatively simple and cost effective to measure when compared to phytoplankton abundance and is thus routinely included in many surveys. Here we collate 173, 333 records of chlorophyll a collected since 1965 from Australian waters gathered from researchers on regular coastal monitoring surveys and ocean voyages into a single repository. This dataset includes the chlorophyll a values as measured from samples analysed using spectrophotometry, fluorometry and high performance liquid chromatography (HPLC). The Australian Chlorophyll a database is freely available through the Australian Ocean Data Network portal (https://portal.aodn.org.au/). These data can be used in isolation as an index of phytoplankton biomass or in combination with other data to provide insight into water quality, ecosystem state, and relationships with other trophic levels such as zooplankton or fish

A database of marine phytoplankton abundance, biomass and species composition in Australian waters

There have been many individual phytoplankton datasets collected across Australia since the mid 1900s, but most are unavailable to the research community. We have searched archives, contacted researchers, and scanned the primary and grey literature to collate 3,621,847 records of marine phytoplankton species from Australian waters from 1844 to the present. Many of these are small datasets collected for local questions, but combined they provide over 170 years of data on phytoplankton communities in Australian waters. Units and taxonomy have been standardised, obviously erroneous data removed, and all metadata included. We have lodged this dataset with the Australian Ocean Data Network (http://portal.aodn.org.au/) allowing public access. The Australian Phytoplankton Database will be invaluable for global change studies, as it allows analysis of ecological indicators of climate change and eutrophication (e.g., changes in distribution; diatom:dinoflagellate ratios). In addition, the standardised conversion of abundance records to biomass provides modellers with quantifiable data to initialise and validate ecosystem models of lower marine trophic levels

The PREDICTS database: A global database of how local terrestrial biodiversity responds to human impacts

© 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015. The collation of biodiversity datasets with broad taxonomic and biogeographic extents is necessary to understand historical declines and to project - and hopefully avert - future declines. We describe a newly collated database of more than 1.6 million biodiversity measurements from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world

Corrigendum: A database of marine phytoplankton abundance, biomass and species composition in Australian waters (Scientific Data (2016) 3 (160043) DOI: 10.1038/sdata201643))

© The Author(s) 2016. A series of errors in our database were brought to our attention by readers, and have been corrected in an updated version of this database, which is accessible via the AODN at the following link: https://portal.aodn.org.au/search?uuid =75f4f1fc-bee3-4498-ab71-aa1ab29ab2c0 The custodian details of several datasets were incorrect. These fields in the metadata table have been updated to correctly assign P744, P746, P748, and P778 to the Australian Antarctic Division, and P752 to the Royal Belgian Institute of Natural Sciences. Species names and functional group assignments have been changed for a small number of records to fix identified errors. Tripos brevis and Tripos arietinus were spelt incorrectly, and have been duly corrected. Pedinellaceae was wrongly assigned to dinoflagellate as a functional group, and has now been re-assigned to flagellate. The 'Naked flagellate' group has been renamed 'Flagellate' as there is some inconsistency in the use of the term 'Naked flagellate' and what precisely would be included. The functional group 'Other', has also been excluded as this contained data that was not necessarily phytoplankton but had been found in phytoplankton counts. The macroalgae Murrayella australica, Cladophora spp., Chlorohormidium sp., Eudorina spp., Tribonema spp., Chlorohormidium spp. were also removed. In addition to these corrections, three datasets have been extended to include more recently acquired data: P 597 IMOS Australian Continuous Plankton Recorder survey (ongoing dataset, 59089 new records as of 2016-08-31); P599 IMOS National Reference Stations (ongoing dataset, 14669 new records as of 2016-08-31); and P1068 Great Barrier Reef Expedition 1928-29 (new dataset, 1340 new records). Table 1 provides a summary of the overall change in database contents. (Table Presented). This dataset will continue to grow and will be regularly updated with new data and any further corrections to the data. Users can email imos-planktonatcsiro.au with any comments, which will be reviewed and included in future updates if applicable. The AODN portal will always direct the user to the most recent version, the original version will remain available at http://dx.doi.org/10.4225/69/ 56454b2ba2f79, and interim versions will be available on request

Testosterone modulates gene expression pathways regulating nutrient accumulation, glucose metabolism and protein turnover in mouse skeletal muscle

Testosterone regulates energy metabolism and skeletal muscle mass in males, but the molecular mechanisms are not fully understood. This study investigated the response of skeletal muscle to castration and testosterone replacement in 8-week-old male mice. Using microarray analyses of mRNA levels in gastrocnemius muscle, 91 genes were found to be negatively regulated by testosterone and 68 genes were positively regulated. The mRNA levels of the insulin signalling suppressor molecule Grb10 and the glycogen synthesis inhibitors, protein phosphatase inhibitor-1 and phosphorylase kinase-c, were negatively regulated by testosterone. The insulin-sensitive glucose and amino acid transporters, Glut3 and SAT2, the lipodystrophy gene, Lpin1 and protein targeting to glycogen were positively regulated. These changes would be expected to increase nutrient availability and sensing within skeletal muscle, increase metabolic rate and carbohydrate utilization and promote glycogen accumulation. The observed positive regulation of atrogin-1 (Fbxo32) by testosterone could be explained by the phosphorylation of Akt and Foxo3a, as determined by Western blotting. Testosterone prevented the castration-induced increase in interleukin-1a, the decrease in interferon-c and the atrophy of the levator ani muscle, which were all correlated with testosterone-regulated gene expression. These findings identify specific mechanisms by which testosterone may regulate skeletal muscle glucose and protein metabolism.